Fault detection based on orthogonal local slow features
ZHANG Zhanbo1, WANG Zhenlei1, WANG Xin2
1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University ofScience and Technology, Shanghai 200237, China; 2. Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Abstract:A local time-space regularized slow feature extraction method was developed to improve data-driven fault detection in the chemical industry based on the process dynamics of closed-loop control systems and the local information contained in the data manifold. An objective function was defined based on the local time-space term to obtain a projection matrix and the pre-extraction feature, S. The span of S contains the static information, while the first derivative of the span of S contains the dynamic information. An independent component analysis was used to obtain statistics for S2 and SPE for both spaces for real-time fault detection. A case study on the Tennessee Eastman process shows the validity of this method.
张展博, 王振雷, 王昕. 基于正交局部慢性特征的故障检测方法[J]. 清华大学学报(自然科学版), 2020, 60(8): 693-700.
ZHANG Zhanbo, WANG Zhenlei, WANG Xin. Fault detection based on orthogonal local slow features. Journal of Tsinghua University(Science and Technology), 2020, 60(8): 693-700.
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